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Mathematical Modeling for Identifying Cost-Effective Policy of Municipal Solid Waste Management under Uncertainty

Y. P. Li1*, G. H. Huang1,2, L. Cui3, and J. Liu1

  1. Department of Environmental Engineering, Xiamen University of Technology, Xiamen 361024, China
  2. Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, Saskatchewan S4S 7H9, Canada
  3. Sino-Canada Energy and Environmental Research Center, North China Electric Power University, Beijing 102206, China

*Corresponding author. Tel.: +86 0592 6291520; fax: +86 0592 6291138 E-mail address: (Y.P. Li).


In municipal solid waste (MSW) management, many impact factors, such as waste generation rate, treatment capacity, diversion goal, and disposal cost appear uncertain. These uncertainties can result in difficulties in the long-term planning of MSW management activities. A critical issue that decision makers should mitigate is how to address these uncertainties due to a lack of knowledge founded on an incomplete characterization, understanding or measurement of MSW systems. In this study, an inexact twostage waste management (ITWM) model is developed for planning long-term MSW management in the City of Changchun, China. The ITWM model incorporates the techniques of interval-parameter programming (IPP) and two-stage stochastic programming (TSP) within an integer programming framework, such that uncertainties expressed as both intervals and probabilities can be reflected; it can also analyze different policy scenarios that are associated with different economic penalty levels. Two cases related to different waste management policies are examined, generating varied levels of waste-management cost and system-failure risk. The results obtained are valuable for addressing issues of waste diversion and capacity expansion with a minimized system cost. They also suggest that the developed model be meaningful for real-world planning problems and the practicality of this approach can be extended to other environmental planning applications containing significant sources of uncertainty.

Keywords: MSW management, optimization, planning, policy analysis, uncertainty

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